In the manufacture of integrated circuits, photolithography, or lithography, is typically used to transfer patterns relating to the layout of an integrated circuit onto a wafer substrate, including, but not limited to, materials such as silicon, silicon germainium (SiGe), silicon-on-insulator (SOI), or various combinations thereof. The drive to improve performance of very-large-scale integrated (VLSI) circuits results in increasing requirements to decrease the size of features and increase the density of layouts. This in turn increasingly requires the use of resolution-enhancement techniques (RET) to extend the capabilities of optical lithographic processes. RET includes techniques such as the use of optical proximity correction (OPC), subresolution-assist-feature-enhanced lithography (SRAF) and phase-shifted-mask-enhanced lithography (PSM).
In spite of the spectacular advancement of several forms of Resolution Enhancement Techniques (RET), the iterative Model-Based Optical Proximity Correction (MBOPC) has established itself as a method of choice for compensation of the mask shapes for lithographic process effects. Conventional MBOPC tools work include the following steps in a manner similar to the following. The shapes on the mask design (henceforth referred to as the mask) are typically defined as polygons. A pre-processing step is performed that divides the edges of each mask shape into smaller line segments. At the heart of the MBOPC tool is a simulator that simulates the image intensity at a particular point, which is typically at the center of each of the line segments. The segments are then moved back and forth, i.e. outward or inward from the feature interior, from their original position on the mask shape at each iteration step of the MBOPC. The iteration stops when (as a result of the modification of the mask shapes) the image intensity at these pre-selected points matches a threshold intensity level, within a tolerance limit.
While the quality of the OPC may improve as the number of segments increases, the efficiency of an MBOPC tool may decrease as the number of segments it simulates and iterates over in each iterative step increases. The number of segments in turn depends on the number of edges in each mask shape. Therefore, it is desirable that the segments that are corrected are only those necessary to obtain the desired lithographic quality.
Segmentation is typically performed in two ways. The first type of segmentation depends on a particular mask shape itself. This type of segmentation tries to capture the variations of the shapes such as corners, jogs, etc. An example of such segmentation is illustrated in FIG. 1A. Two mask shapes 100 and 110 are shown. In this example, mask shape 100 is segmented by placing nodes a, b, c, . . . x, which define edges of segments, at corners a, d, g, j, m, p, s and v, of the polygon that outlines shape 100, and includes some nodes intermediate between the corners. The lengths of the segments and spacing of intermediate nodes is typically determined by criteria such as mask manufacturability constraints and the ability of the MBOPC to accurately reproduce mask shapes on the wafers. For purposes of illustration, only the segmentation of mask shape 100 is illustrated, but neighboring shape 110 could be segmented in a like manner.
A second type of segmentation is imposed on a mask shape by its neighboring shapes. This type of segmentation scheme uses a local space and width dependence. An example of such a segmentation is illustrated in FIG. 1B. For example, mask shape 200 may be segmented by the proximity of a neighboring mask shape 210 in which portions of the neighboring shape 210 are positioned a distance D less than an pre-determined threshold spacing distance. The threshold spacing rule results in the creation of nodes aa, bb, cc, dd, ee and ff on the affected mask shape 200, defining line segments connecting those nodes. The two types of fragments illustrated in FIG. 1A and FIG. 1B are referred to hereinafter as primary fragments, or fragmentations, and the associated nodes are referred to as fragmentation points.
In additions to the fragmentation points shown in both FIG. 1A and FIG. 1B above, some MBOPC methodologies advocate additional secondary fragmentations around each of the primary fragmentations The secondary fragmentation provide for smoother OPC convergences. Referring to FIG. 2A, a set of mask edges 220 are fragmented by primary nodes A, B, C, D, E, F, G. The resulting simulated wafer shape, or image, 221 is the curved line that is superimposed on the mask shape 220. It can be seen that the features of the desired wafer shape, such as corner C and edges AB and EDC are not very well reproduced by the simulated image 221. In FIG. 2B, the mask edges 220 are shown again with the same primary nodes A through G. In addition, a set of secondary nodes B′, B″, C′, C″, D′, D″, E′, E″, F′ and F″, are inserted around the primary nodes A, B, C, D, E and F, respectively, creating finer fragmentations defined by primary and secondary nodes. The presence of the secondary nodes allow improved simulation accuracy compared to use of primary nodes alone. The resulting simulated wafer shape 222 more closely matches the mask shape 220 than the simulated wafer shape 221 based on the primary node fragmentation of FIG. 2A. FIG. 3 illustrates a flow diagram of the process of fragmentation according to a conventional OPC fragmentation scheme. First, a mask layout is provided, for example, by providing a list of shapes (Block 301). Then, for each shape in the list (Block 302), the following steps are performed:
Determine an effective region of interaction (hereinafter simply referred to as ROI) (Block 303) for a given shape i, according to pre-determined spacing and width rules. The effective ROI is the region enclosed by a boundary that is at a distance beyond which a feature outside of that boundary in the layout does not have a substantial effect on the optical process of imaging a particular feature. Stated another way, features outside of the ROI boundary will have substantially insignificant effects, for practical purposes, on the optical process of imaging the particular feature around which the ROI is formed. The optical factors used to define the optical process conditions may include, but are not limited to, wavelength of illumination light, numerical aperture, resist properties, etc. For a given shape i, fragment the shape according to its own polygon corners (Block 304).
Next, fragment shape i according to the proximity of other shapes' edges within the ROI (Block 305). The fragments formed according to Blocks 304, 305 are referred to as primary fragments.
Then, after all shapes have been fragmented, according to another set of rules the fragmentation is cleaned up (Block 306). The rules for cleaning up the fragmentation are typically determined by mask manufacturability and process constraints. Examples of such rules include, but are not limited to, minimum feature size, minimum line width, minimum spacing, etc.
The position at which an edge prints is influenced by other nearby mask polygons. Large perturbing features have a stronger influence than small features, but in general the interaction will fall off with increasing separation. Partially coherent image formation is a nonlinear process, so the falloff in interaction is not a fixed function of distance. However, the general scaling behavior is that of the so-called lens impulse response function, also known as the Airy function. Mathematically, the Airy function is [J1(2π·s)/(π·s)]2, where J1 is the first Bessel function, and s is a dimensionless position coordinate in the image plane, defined as s=x·NA/λ, where x is the position as measured in conventional length units and NA is the effective numerical aperture of the lithographic system, and λ is the wavelength of the illumination light.
Although the interaction between features is nonlinear, one can say that feature to feature interaction will decrease with separation at a rate between the envelope of the Airy function, which falls as the cube of separation distance, d, and the square-root of this envelope, which falls as approximately d3/2, as illustrated in the plots shown in FIG. 4. The former case corresponds to completely incoherent interaction 12, and the latter case to completely coherent interaction 14. The curves in FIG. 4 assume an ideal lens; additional weak interaction at longer distance scales may also be present, for example, due to lens flare. The plotted curves are normalized, so that the area of the interacting, perturbing feature is not considered. However, because interaction falls with increasing separation between features, it becomes acceptable to neglect the influence of small details in interacting, perturbing shapes that are appreciably distant from the shape or feature undergoing fragmentation.
It would be clear from FIG. 1B that any details in a mask feature would interact during the fragmentation procedure to create corresponding segmentations on a neighboring mask shape. Some of those fragments may be created by variations of a neighboring shape that is quite far away from the main shape. As can be seen in FIG. 4, some of those distant fragments may have very little impact on the resulting wafer image simulation. Such segmentations would eventually contribute to the inefficiency of the MBOPC. This inefficiency is exacerbated by the fact, as illustrated in FIG. 4, that the further away a neighboring shape is located, the smaller the proximity effects on a particular mask shape. However, in the conventional OPC methodology, there does not exist any process to take advantage of the above fact.
The effect of the numerous fragmentations due to neighboring shapes is getting worse with technology because of factors such as the following:                1. As the lithographic process is moving deep into sub wavelength technology, and the same wavelength is being used for smaller and smaller technology, the number of neighboring shapes within the Region of Interaction (ROI), which is controlled by the factor λ/NA, is getting larger and larger.        2. For applications of double exposure mask technology, such as the alternating PSM mask at the critical levels, the effects of optical flare may play a significant role. This means that MBOPC software tools would need to consider a larger ROI than for previous technologies. Thus, more neighboring shapes would be now placed within the ROI than before.        3. Many RET require more coherent illumination and this expands the ROI. The result is that more shapes have a lithographic interaction with a given shape of interest, resulting in an increase in fragmentation of a given shape.        
Accordingly, it would be desirable to provide a method for segmenting mask shapes in a manner that improves the efficiency of the MBOPC while not reducing the quality or effectiveness of the OPC.